115 citations · 310 across the 11 of their papers we have counts for
11 papers
Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks
Paul Almasan, Krzysztof Rusek, Shihan Xiao +4
Storing network traffic data is key to efficient network management; however, it is becoming more challenging and costly due to the ever-increasing data transmission rates, traffic…
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters
Guillermo Bernárdez, José Suárez-Varela, Xiang Shi +4
Congestion Control (CC) plays a fundamental role in optimizing traffic in Data Center Networks (DCN). Currently, DCNs mainly implement two main CC protocols: DCTCP and DCQCN. Both…
MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering
Guillermo Bernárdez, José Suárez-Varela, Albert López +5
Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based sol…
Leveraging Spatial and Temporal Correlations for Network Traffic Compression
Paul Almasan, Krzysztof Rusek, Shihan Xiao +4
The deployment of modern network applications is increasing the network size and traffic volumes at an unprecedented pace. Storing network-related information (e.g., traffic traces…
RouteNet-Fermi: Network Modeling with Graph Neural Networks
Miquel Ferriol-Galmés, Jordi Paillisse, José Suárez-Varela +6
Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complex…
Network Digital Twin: Context, Enabling Technologies and Opportunities
Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse +13
The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail…
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies
Carlos Güemes-Palau, Paul Almasan, Shihan Xiao +4
The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is increasing the complexity of managing modern communication networks. As a resul…
Digital Twin Network: Opportunities and Challenges
Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse +13
The proliferation of emergent network applications (e.g., AR/VR, telesurgery, real-time communications) is increasing the difficulty of managing modern communication networks. Thes…
Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities
José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés +8
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e.g., chemistry, biology, recommendation systems).…
ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning
Paul Almasan, Shihan Xiao, Xiangle Cheng +3
Wide Area Networks (WAN) are a key infrastructure in today's society. During the last years, WANs have seen a considerable increase in network's traffic and network applications, i…
Improving Text-to-SQL with Schema Dependency Learning
Binyuan Hui, Xiang Shi, Ruiying Geng +4
Text-to-SQL aims to map natural language questions to SQL queries. The sketch-based method combined with execution-guided (EG) decoding strategy has shown a strong performance on t…